The Datasets
- Movement Data
- Hunting Events
- FCM Data
Movement Data
- Contains the location of the 40 collared deer
- Period: Feb 2020 - Feb 2023
- Movement is tracked in hourly intervals
Hunting Events
- Contains location and date of hunting events
- Observations: 720 events
- 532 Observations with complete timestamp
FCM Data
Contains information of 809 faecal samples, including:
- the FCM level [ng/g]
- the time and location of sampling
- to which deer the sample belongs
- when the defecation happened
Main Challenges
Uncertainty, due to:
- Hunting Events are reported as single timestamp
- Location of Deer reported hourly
- Collared deer are not in the proximity of the reported hunts most of the time
- Curse of high dimension: Locations of Deer are likely to have a big distance to Hunting Events in one dimension (two spatial, one temporal)
Distance Approximation
Deer location at the time of hunting event is approximated by linear interpolation:
Relevant Hunting Events
To identify relevant Hunting Events respective to a given FCM Sample, we introduce three selection parameters:
- Gut retention time (GRT) target [hours]: Target Delay between Stress Event and Defecation
- Gut retention time (GRT) thresholds [hours]: maximum temporal Distance between respective Deer and Hunting Event, with the Minimum beeing Zero
- Distance threshold [km]: Maximum spatial Distance between respective Deer and potential Hunting Event
The Most Relevant Hunting Event
Among the relevant hunting events, the most relevant one is defined by one of the three introduced proximity criteria:
- the closest in time to GRT = 19 hours (“closest in time”)
- the closest in space (“nearest”)
- the one with the “highest score”
Illustration
A hunting event is considered relevant to a FCM sample, if
- the time difference between experiencing stress (hunting) and defecation is between the GRT thresholds, and
- the distance between the deer and the hunting event is \(\leq\) distance threshold.
The Scoring Function
we define the Scoring function as following:
\[
S(d, t) \propto \begin{cases}
\frac{1}{d^2} \cdot f_\textbf{t}(t), t \sim \mathcal{N}(\mu, \sigma^2) &|t \leq \mu \\
\frac{1}{d^2} \cdot f_\textbf{t}(t), t \sim \mathcal{Laplace}(\mu, b) &|t > \mu
\end{cases}
\] where:
\[
\begin{align*}
d & \text{: Distance } \\
t & \text{: Time Difference } \\
\mu & \text{: GRT target = 19 hours }
\end{align*}
\]
The Scoring Function
The marginal effects of distance and elapsed time since challenge on the score:
The Fused Data
Finish Datasets
We suggest three different Datasets for Modelling
| 1 |
0 |
36 |
10 |
closest in time |
35 |
149 |
| 2 |
0 |
36 |
10 |
nearest |
35 |
147 |
| 3 |
0 |
200 |
15 |
score |
36 |
223 |